If you don't mind being old-school, the data is ASCII text, and you're tired of some of CSV's little issue, then ASCII has the FS, GS, RS, and US control characters - specifically intended for such uses. Micro conceptual overhead, none of the CSV issues which screw up many *nix text-handling programs and little script files, and a decent modern filesystem can handle the compression separately.
There are many reasons why CSV is flawed for the purposes of storing tabular data (e.g. loss of column type information) but the alternatives are just so unergonomic that CSV remains a viable choice in many situations.
> If you don't mind being old-school, the data is ASCII text, and you're tired of some of CSV's little issue, then ASCII has the FS, GS, RS, and US control characters
I have used those before, and yet I still had those characters appear in data. The only places I'd ever seen them were in the wiki page and in customer delivered data. Absolute pain to dig through and remove.
On top of that "if your data is ASCII" is something I'd be nervous about for many use cases even if it is right now.
Beyond that, then you need everyone to swap out their parsing to use those characters.
CSV is fine until it totally blows up in your face. All it takes is one "oh it's fine we'll use awk" stage somewhere or a CSV parser that isn't good enough and one person to put a newline where nobody had expected it before.
Oh, yes - which is why I emphasized "is". But ASCII text is easy to test for, which lets you fast-track into exception handling - "Tell Sales that Customer data is not as represented", "Trouble-shoot internal data source", etc.
(My experience is that substantial Customer data is never, ever as initially represented. Nor as represented after you point out the first set of issues with it. Nor as represented after you point out the second set of issues. Nor as...)
Oh with that I mean the ASCII control characters appearing in inputs. So some columns would have record end markers in for example.
If I'm able to make everyone dealing with the reading and writing add specific characters to be used for start/end/etc I'd rather just tell them to swap to a parquet reader unless they've got a really good reason.
Feather is a layer on top of arrow and was a proof of concept (so I'm not sure how heavily it's used now), and arrow is fast becoming the interchange format. It's exactly laid out as things will be in memory - which means zero copy for shuttling it around from one place to another. I _think_ there is less support for feather but that is likely changing as everything converges.
Parquet should be
* Faster to write * Faster to read (even if you're reading the whole file, which actually isn't required, the format helps you read just sections of the columns you need) * Smaller * Better at handling actual floating points
than CSV, while having actual standards alongside it. Be a little wary of pandas guessing the right column types for you if you're creating partitioned files btw.
When you're working with pandas, etc (check out Dask) you can pretty much just swap out some reading and writing functions. You can also use pyarrow directly if you need to be very careful about column types.
For your use case you may want to explicitly use a single column for the features that is a list, I'm not sure if that's better/worse than having so many columns. If a reader may want to find just some images where a small subset of features are > X, you might benefit from multiple columns so that the reader only processes the data it needs.
Worth testing out, but I expect you should be able to try it out in an afternoon if you're already working with pandas/similar. Just install pyarrow and use a to_parquet. Things like dask (or straight pyarrow) give you partitioned files as output if you want too, if there's a useful column or columns to split on https://arrow.apache.org/docs/python/parquet.html#partitione...
Parquet is great, but it’s simply nowhere near as ubiquitous as CSV.
What’s the Parquet equivalent of going to the store to buy Mentos and Diet Coke now?
It's easy to forget just how much analytic "stuff" Excel still powers.
Structure packing[1] and consideration of locality of reference[2] would need to be applied for high performance applications where a computer scientist has considered the algorithm needing to be implemented and the most efficient data format that the source data would need to be provided in.